In the addon of the session, we counted the charging stations located within North-Rhine Westphalia (NRW). Still, we did not show how to get a point layer of NRW charging stations (“charger_nrw”).
Subset the data file yourself by relying on the spatial information of the filecharging_points_ger.csv and a polygon of NRW.
There are two ways to achieve this. How many chargers are located within
NRW?
charging_points_ger.csv (remember to adjust the crs) in the
./data folder and polygons of NRW. For the latter, you can
again use the osmdata syntax.
There are two functions you can explore: sf::st_join and
sf::st_intersection(). The default of
sf::st_join() will leave you with a ‘left-join’ and returns
a data object with all chargers and matching district information for
those which are located within NRW. You can reset the option to perform
an ‘inner-join’ and keep only the observation which lay within the
predefined area
(sf::st_join(x , y, join = "", left = FALSE)).
# load charger
charger_ger <-
# Read charging station points datae
readr::read_delim("./data/charging_points_ger.csv",
delim = ";") %>%
# Filter out rows with missing longitude or latitude
dplyr::filter(!is.na(longitude) & !is.na(latitude)) %>%
# Convert data frame to sf object
sf::st_as_sf(coords = c("longitude", "latitude"), crs = 4326) %>%
# Reproject the spatial data to the desired CRS (Coordinate Reference System)
sf::st_transform(crs = 3035)
## Warning: One or more parsing issues,
## call `problems()` on your
## data frame for details, e.g.:
## dat <- vroom(...)
## problems(dat)
## Rows: 60560 Columns: 7
## ── Column specification ──────
## Delimiter: ";"
## chr (3): operator, federal...
## dbl (4): latitude, longitu...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# use the OSM function
nrw <-
osmdata::getbb(
"Nordrhein-Westfalen",
format_out = "sf_polygon"
) %>%
.$multipolygon %>%
sf::st_transform(3035)
# option 1
charger_nrw <-
charger_ger %>%
# Subset point data to sampling area
sf::st_intersection(nrw)
## Warning: attribute variables
## are assumed to be spatially
## constant throughout all
## geometries
# option 2
# spatial join
charger_nrw <-
charger_ger %>%
sf::st_join(
# point layer nrw
nrw,
# chose intersect or within
join = sf::st_intersects,
# option FALSE will
# keep only the chargers
# which could be joined
left = FALSE
)
nrow(charger_nrw)
## [1] 11081
# 11081 chargers in NRW
Did the operationalization of train station accessbility convince you? The INKAR data base offers another approach: Proportion of residents with max. 1000m linear distance to the nearest public transport stop in the district. We do have everything to create this indicator. You can run this code to load all the data you need. What is the mean share of residents with max. 1000m linear distance to the nearest train station in a 5km neighbourhood of our fake respondents?
nrw <-
osmdata::getbb(
"Nordrhein-Westfalen",
format_out = "sf_polygon"
) %>%
.$multipolygon %>%
sf::st_transform(3035)
set.seed(1234)
fake_coordinates <-
sf::st_sample(nrw, 1000) %>%
sf::st_sf() %>%
dplyr::mutate(
id_2 =
stringi::stri_rand_strings(10000, 10) %>%
sample(1000, replace = FALSE)
)
nrw_pt_trainstops <- sf::st_read("./data/nrw_pt_osmtrainstops.shp", crs = 3035)
inhabitants_ger <-
z11::z11_get_100m_attribute(Einwohner)
sf point layer of the raster object you can use
terra::as.points() %>% sf::st_as_sf(). To rasterize the
object you can use
terra::rast(vals = .$colname, resolution = 100).
# Extract inhabitants points for North Rhine-Westphalia (NRW)
nrw_pt_inhabitants <-
inhabitants_ger %>%
terra::crop(., nrw) %>%
# convert to points
terra::as.points() %>%
# convert to sf object
sf::st_as_sf()
# Find the nearest train station for each grid centroid
nearest_station <-
sf::st_nearest_feature(nrw_pt_inhabitants, nrw_pt_trainstops)
# Calculate distances from inhabitants to the nearest train station
distances <- sf::st_distance(nrw_pt_inhabitants,
nrw_pt_trainstops[nearest_station,],
by_element = TRUE)
# Create a raster representing population with train access within 1000m for NRW
nrw_rast_inhabitants_access <-
nrw_pt_inhabitants %>%
dplyr::mutate(train_access = ifelse(as.numeric(distances) <= 1000, lyr.1, 0)) %>%
terra::rast(vals = .$train_access, resolution = 100)
## Warning: [setValues] values
## were recycled
# Create a raster representing population for NRW
# NOTE: You could also use the 'old' raster. However, by converting the
# data the extent of the raster layer might not fit anymore and needs adjustments.
nrw_rast_inhabitants <-
nrw_pt_inhabitants %>%
terra::rast(vals = .$lyr.1, resolution = 100)
## Warning: [setValues] values
## were recycled
# Extract population within 5km buffers around each respondent
population_buffers <-
terra::extract(
nrw_rast_inhabitants,
fake_coordinates %>%
sf::st_buffer(5000) %>%
terra::vect(),
fun = mean,
na.rm = TRUE
)
# Extract population within 5km buffers considering train access within 1000m
population_access_buffers <- terra::extract(
nrw_rast_inhabitants_access,
fake_coordinates %>%
sf::st_buffer(5000) %>%
terra::vect(),
fun = mean,
na.rm = TRUE
)
# Combine population data with train access information
linked_df <-
fake_coordinates %>%
dplyr::mutate(
population = population_buffers[[2]],
population_trainaccess = population_access_buffers[[2]],
share_access = population_access_buffers[[2]] / population_buffers[[2]]
)
# Summary of the new data
summary(linked_df)
## geometry
## POINT :1000
## epsg:3035 : 0
## +proj=laea...: 0
##
##
##
## id_2
## Length:1000
## Class :character
## Mode :character
##
##
##
## population
## Min. :18.71
## 1st Qu.:22.20
## Median :28.78
## Mean :28.42
## 3rd Qu.:33.67
## Max. :41.75
## population_trainaccess
## Min. : 3.409
## 1st Qu.: 5.301
## Median : 8.328
## Mean : 8.204
## 3rd Qu.:10.704
## Max. :16.084
## share_access
## Min. :0.1698
## 1st Qu.:0.2404
## Median :0.2854
## Mean :0.2795
## 3rd Qu.:0.3174
## Max. :0.3879
# Mean value: 28,94%